84 citations · 212 across the 15 of their papers we have counts for
17 papers
Structure-Aware Rendering: How Code Reveal Shapes Programmers' Visual Attention
Xiaotian Su, Jan Brasser, David Robert Reich +2
AI coding interfaces present generated code either all at once or token-by-token. These rendering strategies reflect model generation rather than how programmers actually read code…
Notrix: Understanding Machine Learning Solutions Across Computational Notebooks at Scale
Xiaotian Su, Hongxin Fu, Xiaoyu Zhang +1
Computational notebooks make problem-solving visible, but typically only one notebook at a time. Meanwhile, in data science platforms like Kaggle, one competition can accumulate hu…
[MM/AI] Mental Models in Human-AI Interaction: Methods and Challenges in the Generative and Agentic AI Era (Workshop)
Téo Sanchez, Bhada Yun, Prerna Ravi +6
The mental model construct is widely used in HCI to refer to the knowledge structure people hold in order to reason about and interact with computing systems. Yet it is often opera…
NL2Scratch: An Executable Benchmark and Evaluation for Block-Based Programming
Heejin Do, Alexandre Ballenghien, Yang Wu +1
Block-based programming environments such as Scratch are widely used in early programming education, yet natural-language-to-code (NL2Code) research has focused primarily on text-b…
Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education
Junling Wang, Boqi Chen, Heejin Do +3
AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully represent the pedagogical concept…
When Should Teachers Control AI Generation for Mathematics Visuals?
Zhengxu Li, Junling Wang, April Yi Wang
Generative AI has the potential to help teachers rapidly create classroom-ready visual materials, particularly in mathematics where diagrams and visual representations must be peda…
GeoDial: A Multimodal Conversational Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns
Sankalan Pal Chowdhury, Junling Wang, Donya Rooein +2
Several educational domains rely heavily on diagrams and visual cues, yet most existing tutoring datasets are limited to text-only interactions. This limits the development of AI t…
AI Phenomenology for Understanding Human-AI Experiences Across Eras
Bhada Yun, Evgenia Taranova, Dana Feng +2
There is no 'ordinary' when it comes to AI. The human-AI experience is extraordinarily complex and specific to each person, yet dominant measures such as usability scales and engag…
"Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbots
Irene Hou, Zeyu Xiong, Philip J. Guo +1
Instructors are increasingly experimenting with AI chatbots for classroom support. To investigate how instructors adapt chatbots to their own contexts, we first analyzed existing r…
The Stress of Improvisation: Instructors' Perspectives on Live Coding in Programming Classes
Xiaotian Su, April Wang
Live coding is a pedagogical technique in which an instructor writes and executes code in front of students to impart skills like incremental development and debugging. Although li…
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software
Anjali Khurana, Xiaotian Su, April Yi Wang +1
Large Language Model (LLM)-based in-application assistants, or copilots, can automate software tasks, but users often prefer learning by doing, raising questions about the optimal…
DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition
Shuai Ma, Junling Wang, Yuanhao Zhang +2
Decomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study metho…
Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design
Qian Zhu, Dakuo Wang, Shuai Ma +4
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One v…
"Don't Step on My Toes": Resolving Editing Conflicts in Real-Time Collaboration in Computational Notebooks
April Yi Wang, Zihan Wu, Christopher Brooks +1
Real-time collaborative editing in computational notebooks can improve the efficiency of teamwork for data scientists. However, working together through synchronous editing of note…
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Chengbo Zheng, Dakuo Wang, April Yi Wang +1
Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists' burden on data p…
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
April Yi Wang, Dakuo Wang, Jaimie Drozdal +6
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code,…
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Wang, Ban Kawas +3
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer val…